This is in response to the thread on recommender system implementation in
scikit-learn. I would also like to know if any of the scikit-learn contributors
are willing to mentor a project which implements basic recommender system
algorithms - collaborative filtering (user-based/item-based/matrix
factorization) for Google Summer of Code. I feel the lack of a scalable,
extensible open-source recommendation engine in python is an interesting gap to
fill and would like to try my hand at it during GSOC. There are a couple of
interesting problems to address in this case like how to design a
recommendation engine that conforms to the design of scikit-learn package as
much as possible. Some of the other challenges are implementing support for
Sparse matrix operations.
Thanks,
Nikhil
________________________________
From: MIT SHAH [mitk1s...@gmail.com]
Sent: Sunday, February 02, 2014 9:39 AM
To: scikit-learn-general@lists.sourceforge.net
Subject: [Scikit-learn-general] Contributing in a New Topic : Recommender
Systems
Hi,
I want to know whether there are algorithms on "Recommender Systems" in
scikit-learn. I didn't found this topic in documentation. If not, I would like
to contribute on this topic.
Please guide me.
Thanks !!
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